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These aquatic robots are more advanced than they look!🐬 Early versions developed by Walt Disney Imagineering started as simple underwater drones, essentially radio-controlled machines, using jet pumps for propulsion and servo motors for control. Functional, but clearly nothing new. Then came the biomimetic stuff. Larger prototypes began mimicking dolphins,...

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Lukas Ziegler

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67,279 次观看 • 9 个月前 •via X (Twitter)

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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,090 次观看 • 9 个月前

Magnetic levitation for surgical robots! 🧲 A research team working on minimally invasive surgery just introduced a remote magnetic levitation to move things inside our bodies. Their main goal is to control guidewires and catheters inside the body using magnetic fields. But levitation is unstable and extreme, it forces them to solve fundamental control problems they can't avoid. They have to handle strong magnetic field nonlinearities, develop real-time invertible models, and create control methods that work on any eMNS (electroMagnetic Navigation System) and any object. The real challenge is the position and orientation feedback. You can control a magnet outside the body, but once a catheter or drug-delivery robot is inside a patient, you can't see it. There's no vision, but just magnetic fields and current measurements. That's huge news coming out of ETH Zurich! Here's the paper: Mentioned all authors on Li, but coulnd't find them here! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:20

Magnetic levitation for surgical robots! 🧲 A research team working on minimally invasive surgery just introduced a remote magnetic levitation to move things inside our bodies. Their main goal is to control guidewires and catheters inside the body using magnetic fields. But levitation is unstable and extreme, it forces them to solve fundamental control problems they can't avoid. They have to handle strong magnetic field nonlinearities, develop real-time invertible models, and create control methods that work on any eMNS (electroMagnetic Navigation System) and any object. The real challenge is the position and orientation feedback. You can control a magnet outside the body, but once a catheter or drug-delivery robot is inside a patient, you can't see it. There's no vision, but just magnetic fields and current measurements. That's huge news coming out of ETH Zurich! Here's the paper: Mentioned all authors on Li, but coulnd't find them here! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

121,595 次观看 • 3 个月前

Robots helping with grain bins! 🌾 That's quite an interesting use case for robots. Let's break it down! So grain bins are explosion hazards. The interior is classified as an Ex-zone due to dust that can ignite. That's just the beginning. It takes less than five seconds to become helplessly trapped in flowing grain, less than thirty seconds to be fully engulfed. Purdue data shows 22 fatalities in confined spaces such as grain bins. That's why it's a great application for robotics. This robot from Grain Weevil is about two feet long, weighs 50 pounds, and uses two motorized augers for propulsion. As it moves, it creates a "controlled avalanche", grain breaks up and levels itself using gravity. It handles the work farmers do manually: leveling grain, breaking crusts and bridges that form from moisture, removing grain from walls, and feeding extraction augers during unloading. Current version runs 1-2 hours on a 15-20 minute charge. Farmers control it from outside the bin with a remote and camera system. Full autonomy is coming by fall 2026. GPS and Wi-Fi don't penetrate steel grain bins. Traditional autonomous machines fail. Grain Weevil is overcoming this by mounting small magnetic radio devices outside the grain bin, triangulating space so the robot maps the interior in real time. 📻 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:47

Robots helping with grain bins! 🌾 That's quite an interesting use case for robots. Let's break it down! So grain bins are explosion hazards. The interior is classified as an Ex-zone due to dust that can ignite. That's just the beginning. It takes less than five seconds to become helplessly trapped in flowing grain, less than thirty seconds to be fully engulfed. Purdue data shows 22 fatalities in confined spaces such as grain bins. That's why it's a great application for robotics. This robot from Grain Weevil is about two feet long, weighs 50 pounds, and uses two motorized augers for propulsion. As it moves, it creates a "controlled avalanche", grain breaks up and levels itself using gravity. It handles the work farmers do manually: leveling grain, breaking crusts and bridges that form from moisture, removing grain from walls, and feeding extraction augers during unloading. Current version runs 1-2 hours on a 15-20 minute charge. Farmers control it from outside the bin with a remote and camera system. Full autonomy is coming by fall 2026. GPS and Wi-Fi don't penetrate steel grain bins. Traditional autonomous machines fail. Grain Weevil is overcoming this by mounting small magnetic radio devices outside the grain bin, triangulating space so the robot maps the interior in real time. 📻 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,089 次观看 • 2 个月前

Octopus inspired robot! 🐙 Researchers at the National University of Singapore have created a streamlined octopus-inspired swimming robot capable of replicating the animal’s agile movement through water. Unlike previous designs that require eight actuators for each arm, this robot simplifies the mechanics using just two motors and an umbrella-like mechanism to coordinate its eight soft arms. As it swims, the robot’s arms gently open to reduce drag, then quickly close to generate thrust mimicking the jet-propulsion used by real octopuses. In lab tests, it reached speeds up to 314 mm/s. This soft robotic platform not only matches the swimming performance of more complex systems but also offers greater energy efficiency. Pretty cool nature-inspired robot application! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:42

Octopus inspired robot! 🐙 Researchers at the National University of Singapore have created a streamlined octopus-inspired swimming robot capable of replicating the animal’s agile movement through water. Unlike previous designs that require eight actuators for each arm, this robot simplifies the mechanics using just two motors and an umbrella-like mechanism to coordinate its eight soft arms. As it swims, the robot’s arms gently open to reduce drag, then quickly close to generate thrust mimicking the jet-propulsion used by real octopuses. In lab tests, it reached speeds up to 314 mm/s. This soft robotic platform not only matches the swimming performance of more complex systems but also offers greater energy efficiency. Pretty cool nature-inspired robot application! 👀 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

16,757 次观看 • 1 个月前

Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:38

Robots moving railcars! 🚃 Did you know that KUKA is also in the mobile robots business? Yes, you heard that right. And not just any robots. Robots that carry up to 56 tons! 🏋🏼 The railcar production process is highly complex, particularly when moving massive cars sideways within a plant. This challenge has been addressed at the Siemens facility in Germany using the OmniMove system from KUKA. The system allows for precise positioning, enabling the units to lift, move, and lower railcars with remarkable accuracy. With Mecanum wheels, the OmniMove units can move seamlessly in any direction, making the process incredibly efficient. Together, these units are capable of lifting up to 56 tons, streamlining heavy railcar production. This one is clearly tele-operated, but just looking at it makes me feel safer when I see a human in the loop. What a great use case for mobile robots! 😮‍💨 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

19,285 次观看 • 3 个月前

HUGE FUNDING JUMP FOR AUTONOMOUS KILLER ROBOTS BREAKING NEWS: The Pentagon this week filed a call for a 24,000% cash increase (not a misprint) for a unit that creates flying killer robots for use in a war on China. Many of the machines will be able to attack autonomously, rather than being remote controlled. The step was revealed in a request filed this week, for US$55bn to fund a new unit called the Defense Autonomous Warfare Group. This is “the largest single commitment to autonomous warfare in history. Its scale should be applauded,” said former CIA director David Petraeus. . PLANNED WAR ON CHINA The DAWG is a new Pentagon department, built on top of a Joe Biden initiative to create drones for use on a planned war on China. The funding demand is so large because the US last December banned the purchase of drones and components from China, the world leader in that area. It would be problematic to ask the Chinese to continue to provide equipment for the US to use to attack China. The undersecretary for war, Elbridge Colby, has long called for a US war on China, exploiting Taiwan as justification. . WHICH NATIONS BACK KILLER ROBOTS? The Pentagon has long been using the argument that they are responding to China’s use of killer robots, although this appears to be untrue. In 2019, China and the majority of other countries of the world met at the UN to back a motion which said that the granting of power to machines to kill humans was a horrific idea which should be banned globally. But four known militaristic nations were among those which disagreed with the motion: The US, the UK, Russia and Australia. . SCIENTIST'S WARNING In January this year, one of the world’s top scientists, physicist Max Tegmark, said technologists opposing killer robots programs should be commended “for standing up for the principle that AI should never be used to kill people without meaningful human control”. However, that assumes that ethical human-centered thinking will be at the center of decisions—and that is clearly not the case. Military power, global dominance and profit are the ruling motivations. . TRUMP'S ELDEST SONS The Pentagon wants private company participation and is getting it. A top US dronemaker called Powerus is already offering autonomous US drones to Gulf nations. The Arab nations allowed the US and Israel to launch attacks on Iran from their countries and now have to deal with the Iranians defending themselves. Powerus, which has Donald Trump’s two oldest sons on its board, cannot lose. Trump senior has launched an unprovoked attack on Iran from the Gulf nations, and the younger Trumps are now involved with a firm selling autonomous drones for protection. All this, sadly, mitigates against hopes for peace.
2:36

HUGE FUNDING JUMP FOR AUTONOMOUS KILLER ROBOTS BREAKING NEWS: The Pentagon this week filed a call for a 24,000% cash increase (not a misprint) for a unit that creates flying killer robots for use in a war on China. Many of the machines will be able to attack autonomously, rather than being remote controlled. The step was revealed in a request filed this week, for US$55bn to fund a new unit called the Defense Autonomous Warfare Group. This is “the largest single commitment to autonomous warfare in history. Its scale should be applauded,” said former CIA director David Petraeus. . PLANNED WAR ON CHINA The DAWG is a new Pentagon department, built on top of a Joe Biden initiative to create drones for use on a planned war on China. The funding demand is so large because the US last December banned the purchase of drones and components from China, the world leader in that area. It would be problematic to ask the Chinese to continue to provide equipment for the US to use to attack China. The undersecretary for war, Elbridge Colby, has long called for a US war on China, exploiting Taiwan as justification. . WHICH NATIONS BACK KILLER ROBOTS? The Pentagon has long been using the argument that they are responding to China’s use of killer robots, although this appears to be untrue. In 2019, China and the majority of other countries of the world met at the UN to back a motion which said that the granting of power to machines to kill humans was a horrific idea which should be banned globally. But four known militaristic nations were among those which disagreed with the motion: The US, the UK, Russia and Australia. . SCIENTIST'S WARNING In January this year, one of the world’s top scientists, physicist Max Tegmark, said technologists opposing killer robots programs should be commended “for standing up for the principle that AI should never be used to kill people without meaningful human control”. However, that assumes that ethical human-centered thinking will be at the center of decisions—and that is clearly not the case. Military power, global dominance and profit are the ruling motivations. . TRUMP'S ELDEST SONS The Pentagon wants private company participation and is getting it. A top US dronemaker called Powerus is already offering autonomous US drones to Gulf nations. The Arab nations allowed the US and Israel to launch attacks on Iran from their countries and now have to deal with the Iranians defending themselves. Powerus, which has Donald Trump’s two oldest sons on its board, cannot lose. Trump senior has launched an unprovoked attack on Iran from the Gulf nations, and the younger Trumps are now involved with a firm selling autonomous drones for protection. All this, sadly, mitigates against hopes for peace.

Nury Vittachi

13,872 次观看 • 5 个月前

There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://
0:23

There’s not enough hype about Flexion Robotics. They are literally one of the sexiest European companies these days! 🇪🇺 I had a chance to meet the founder Nikita last year and learn his story. He turned down offers from top physical AI companies to build his own. The reason was his specific vision of how robot intelligence should be built to actually scale. So he decided to build one himself. Flexion is building an ultimate brain for humanoid robots. Yesterday they released Reflect v1.0. It’s an intelligence platform for long-horizon autonomous missions. The system has three layers. At the top, a custom vision-language model acts as mission control, observing the robot's camera feed and replanning continuously. Below that, a motion layer combines a VLA trained on real-world data with reinforcement learning-based skills. It translates perception into navigation, scene interaction, and manipulation commands. At the bottom, a whole-body controller runs in real-time, maintaining balance while the robot navigates and performs tasks. I highly recommend them visiting Zurich! 🇨🇭 Nikita Rudin cheapeu bas on the launch! 🎩 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → http://

Lukas Ziegler

23,244 次观看 • 3 个月前

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:26

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 次观看 • 5 个月前

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:34

🚨 BREAKING: Microsoft's first robotics foundation model! 🤯 Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models. Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots. What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback. On the learning side, the model is designed to continually improve during deployment by learning from human feedback. The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data. Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets. The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha. Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

61,026 次观看 • 8 个月前

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.
0:31

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 次观看 • 8 个月前

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:48

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

22,482 次观看 • 6 个月前

This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction
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This is the Scorpion Hexapod, a six-legged robotic scorpion created at Ghent University UGent Campus Kortrijk in Belgium. It was built by students Stephan Flamand Robbe Terryn and Pieterjan Deconinck as part of an Embedded Prototyping / Mechatronics Design project. What it is • A biomimetic robot inspired by the body and movement of a real scorpion • A hexapod, meaning it walks on six legs • A university prototype, not a commercial robot • Built to test animal-inspired movement, sensors and interactive behavior • Designed more for robotics research and education than real-world work Main hardware • 6 walking legs for crawling movement • 2 front claws for the scorpion look • Moving tail with a stinger-style mechanism • Sensors in the body, legs and claws • Arduino-based electronics for control • Front camera and proximity sensing • Battery pack for mobile operation • 3D-printed modules for legs and tail • Laser-cut ABS body parts • Thermoformed shell for the white outer body What it can do • Walk across the floor using its six legs • Move its tail like a real scorpion • React when a person gets close • Detect when someone covers its front sensors • Strike with its tail in the demo • Leave a red mark using a marker pen attached to the stinger • Operate through remote control • Perform some simple autonomous reactions Why it was created • To explore bio-inspired robotics • To show how digital fabrication can produce complex moving robots • To combine 3D printing, laser cutting, Arduino electronics and sensors • To teach students how to design a complete mechatronic system • To improve on an older robotic ant project that had weak autonomy, short battery life and motor problems • To create an interactive robot that reacts to humans in a visible way Important note • It was not built for combat • It was not made for industrial deployment • It is not a military robot • It is an educational robotics prototype made to demonstrate movement, sensing and interaction

Techniahqrobot | humanoid robots

28,176 次观看 • 3 个月前

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:41

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

45,908 次观看 • 3 个月前

Another awesome robotics company raised funds in Europe. 🇪🇺 This time a company that is shipping robots that build! Monumental has raised $32M Series B led by Khosla Ventures, and its fleet of autonomous bricklaying robots has already built more than 100 real structures across Europe. Houses. A school. A community centre. A hotel. Canal walls. Founded by Salar al Khafaji al Khafaji and Sebastiaan Visser, the co-founders of data visualisation firm Silk, acquired by Palantir in 2016, this is a team that knows how to build and sell a company. The technology is serious: → Fleet of 100+ electric autonomous robots operating on live job sites today → Advanced sensors, computer vision and small cranes laying brick to millimetre precision → All driven by Atrium, their proprietary AI software platform → Nearly half of all homes built in the last three months alone, pace is compounding fast The business model is also worth taking a look at. Contractors don't buy the robots. They hire Monumental as an autonomous subcontractor and pay for finished wall. So in the end you only pay for the output. This is the same forward-deployed engineering model Palantir pioneered in software. Monumental brought it to physical robotics years before anyone else in the industry caught on. The backdrop makes this urgent. US construction is short 200,000-400,000 workers every month. Since 1945, US manufacturing productivity rose eightfold. Construction gained just 10%, and has actually declined since the 1960s. LET'S MAKE CONSTRUCTION BUSINESSES SEXY AGAIN! 🧱 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:28

Another awesome robotics company raised funds in Europe. 🇪🇺 This time a company that is shipping robots that build! Monumental has raised $32M Series B led by Khosla Ventures, and its fleet of autonomous bricklaying robots has already built more than 100 real structures across Europe. Houses. A school. A community centre. A hotel. Canal walls. Founded by Salar al Khafaji al Khafaji and Sebastiaan Visser, the co-founders of data visualisation firm Silk, acquired by Palantir in 2016, this is a team that knows how to build and sell a company. The technology is serious: → Fleet of 100+ electric autonomous robots operating on live job sites today → Advanced sensors, computer vision and small cranes laying brick to millimetre precision → All driven by Atrium, their proprietary AI software platform → Nearly half of all homes built in the last three months alone, pace is compounding fast The business model is also worth taking a look at. Contractors don't buy the robots. They hire Monumental as an autonomous subcontractor and pay for finished wall. So in the end you only pay for the output. This is the same forward-deployed engineering model Palantir pioneered in software. Monumental brought it to physical robotics years before anyone else in the industry caught on. The backdrop makes this urgent. US construction is short 200,000-400,000 workers every month. Since 1945, US manufacturing productivity rose eightfold. Construction gained just 10%, and has actually declined since the 1960s. LET'S MAKE CONSTRUCTION BUSINESSES SEXY AGAIN! 🧱 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

74,876 次观看 • 2 个月前

Open-source magnetic tactile sensor for $5! 🧲 Researchers introduced a magnetic tactile sensor that's low-cost, and easy to fabricate, democratizing tactile sensing for robotics. Operating in unstructured environments like homes and offices requires robots to sense forces during physical interaction. Yet the lack of a versatile, accessible tactile sensor has led to fragmented solutions and often force-unaware, sensorless approaches. Building an eFlesh sensor requires four components: a hobbyist 3D printer, off-the-shelf magnets (less than $5), a CAD model, and a magnetometer circuit board. The sensor is 3D printed with magnets embedded in the middle layer. Based on chosen mechanical properties, magnets displace in response to contact forces, measured by a magnetometer underneath. An open-source design tool converts simple OBJ/STL files into 3D-printable STLs. This enables application-specific sensors for robot hands, grippers, quadruped feet, and more. Slip detection generalizes to unseen objects with 95% accuracy. Visual-tactile control policies improve manipulation by 40% over vision-only baselines, achieving 90% success on precise tasks like plug insertion and credit card swiping. All design files, code, trained models, and conversion tools are openly available. Project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:57

Open-source magnetic tactile sensor for $5! 🧲 Researchers introduced a magnetic tactile sensor that's low-cost, and easy to fabricate, democratizing tactile sensing for robotics. Operating in unstructured environments like homes and offices requires robots to sense forces during physical interaction. Yet the lack of a versatile, accessible tactile sensor has led to fragmented solutions and often force-unaware, sensorless approaches. Building an eFlesh sensor requires four components: a hobbyist 3D printer, off-the-shelf magnets (less than $5), a CAD model, and a magnetometer circuit board. The sensor is 3D printed with magnets embedded in the middle layer. Based on chosen mechanical properties, magnets displace in response to contact forces, measured by a magnetometer underneath. An open-source design tool converts simple OBJ/STL files into 3D-printable STLs. This enables application-specific sensors for robot hands, grippers, quadruped feet, and more. Slip detection generalizes to unseen objects with 95% accuracy. Visual-tactile control policies improve manipulation by 40% over vision-only baselines, achieving 90% success on precise tasks like plug insertion and credit card swiping. All design files, code, trained models, and conversion tools are openly available. Project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

289,657 次观看 • 5 个月前

Robotics is following AI’s exact playbook and the Capex explosion is coming next. The first step is already happening, money is flooding into the companies building physical AI. Robotics and physical AI startups raised about $16.3 billion across 492 deals in the first quarter of 2026, roughly 4.5 times the average quarterly funding from 2021–2025. In the first half of 2026, physical AI companies raised $47.4 billion, more than the sector raised across all of 2022–2024 combined. That is how the AI cycle started, venture capital funded the technology first, then companies began spending hundreds of billions on the infrastructure needed to deploy it. Robotics is now moving from research labs into warehouses, factories, auto plants, logistics centers, and defense systems. Goldman Sachs raised its 2035 humanoid robot forecast from 1.38 million units to 6.48 million, with the market potentially reaching $138 billion. The reason is falling costs and Goldman expects average robot prices to decline from about $41,800 in 2025 to $21,300 by 2035. As prices fall, the payback period could shrink from 2.8 years in 2026 to about 1.9 years in 2027, making robots much easier for companies to justify as capital investments. That is when robotics can trigger its own capex cycle. Companies will spend not just on robots but also on factories, sensors, chips, batteries, software, power systems, data centers, and new automated facilities. Amazon is already a major example because its automation program could save roughly $72 billion between 2026 and 2030 and add about 240 basis points to operating margins. Morgan Stanley sees the long term opportunity as even larger, estimating 1 billion humanoid robots and about $7.5 trillion in annual revenue by 2050. And the biggest beneficiaries in all of this will be the picks and shovels companies behind the robots. That includes Nvidia and Renesas for chips, Teradyne for automation, Harmonic Drive for precision gearboxes, and Toyota, Honda, JTEKT, Aisin, and MinebeaMitsumi for manufacturing and motion control components. Bullish on robotics and the picks and shovels behind the next capex cycle and If you enjoyed reading this, make sure to follow Melvin for more robotics and AI insights. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can join for just $1 using the link below.
3:02

Robotics is following AI’s exact playbook and the Capex explosion is coming next. The first step is already happening, money is flooding into the companies building physical AI. Robotics and physical AI startups raised about $16.3 billion across 492 deals in the first quarter of 2026, roughly 4.5 times the average quarterly funding from 2021–2025. In the first half of 2026, physical AI companies raised $47.4 billion, more than the sector raised across all of 2022–2024 combined. That is how the AI cycle started, venture capital funded the technology first, then companies began spending hundreds of billions on the infrastructure needed to deploy it. Robotics is now moving from research labs into warehouses, factories, auto plants, logistics centers, and defense systems. Goldman Sachs raised its 2035 humanoid robot forecast from 1.38 million units to 6.48 million, with the market potentially reaching $138 billion. The reason is falling costs and Goldman expects average robot prices to decline from about $41,800 in 2025 to $21,300 by 2035. As prices fall, the payback period could shrink from 2.8 years in 2026 to about 1.9 years in 2027, making robots much easier for companies to justify as capital investments. That is when robotics can trigger its own capex cycle. Companies will spend not just on robots but also on factories, sensors, chips, batteries, software, power systems, data centers, and new automated facilities. Amazon is already a major example because its automation program could save roughly $72 billion between 2026 and 2030 and add about 240 basis points to operating margins. Morgan Stanley sees the long term opportunity as even larger, estimating 1 billion humanoid robots and about $7.5 trillion in annual revenue by 2050. And the biggest beneficiaries in all of this will be the picks and shovels companies behind the robots. That includes Nvidia and Renesas for chips, Teradyne for automation, Harmonic Drive for precision gearboxes, and Toyota, Honda, JTEKT, Aisin, and MinebeaMitsumi for manufacturing and motion control components. Bullish on robotics and the picks and shovels behind the next capex cycle and If you enjoyed reading this, make sure to follow Melvin for more robotics and AI insights. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, you can join for just $1 using the link below.

Melvin

23,055 次观看 • 23 天前